Vision

A language for system intent.

Cohesive turns accepted product decisions and technical constraints into durable system knowledge. People and agents review that knowledge. Compilers carry it into software that runs.

The Bottleneck Is Intent

As agents make implementation cheaper, more of the work moves toward deciding what the system means, what must remain true, and which tradeoffs are acceptable. Those decisions need a representation that can carry them through repeated change.

Today that intent is spread across tickets, diagrams, source files, tests, schemas, dashboards, and team memory. Each change begins by reconstructing the rules that still matter. Over time, different parts of the system tell slightly different stories.

Cohesive gives those decisions an authored form. A transition states which change is allowed, the invariants it preserves, and the effects it requires. A process describes how work proceeds across decisions, waits, and failures. Relations connect the facts that different parts of the system use.

These definitions share identities in one semantic system graph. Supported code, schemas, interfaces, and checks can be derived from that source, reducing the repeated translation of the same decisions into separate artifacts.

From Exploration To Commitment

People and agents can explore ideas in natural language. When a decision is ready to become part of the system, it crosses a commitment boundary into readable, versioned Cohesive artifacts.

Compilers resolve those artifacts into connected graph state, then produce semantic diffs, diagnostics, generated code, and runtime artifacts. For a specific change, tooling gathers the relevant definitions and dependencies: a transition, the invariants it must preserve, the processes it affects, and the API or storage artifacts derived from it. Those elements remain connected to the rest of the system.

Bind The Graph To What Runs

Bindings connect the graph to databases, services, workflow engines, interfaces, and environments. Domain definitions state the required behavior while a realization supplies an execution strategy. A process might run through a database-backed worker or a workflow engine, provided the selected target satisfies its ordering, durability, recovery, and other requirements.

Repeatable compilation makes a result reproducible. Correctness also depends on the definitions, compiler, bindings, and assumptions behind it. Some claims can be derived, some are backed by tests or runtime observations, and some remain assumptions. Those levels of certainty matter when choosing a realization.

Runtime observations close the feedback loop. They can reveal a failed effect, a stalled process, or behavior that conflicts with an accepted rule. People and agents use that evidence to investigate and propose a correction. A reviewed change updates the source; an observation retains its role as evidence.

The Language Has To Earn Its Place

Adoption can begin with one entity, relation, or process. Each accepted definition gives the next change more context, so the value can grow with the system.

The thesis is empirical. We evaluate whether Cohesive makes relevant behavior easier to understand, surfaces semantic defects earlier, reduces drift between projections, and makes repeated changes safer. Those gains need to justify the cost of authoring and maintaining the extra language layer.

Choose A Lens